1. Field
Embodiments presented herein provide techniques for evaluating image aesthetics. More specifically, embodiments presented herein disclose techniques for determining mappings from objective image attributes to subjective image attributes and using the determined mappings to generate aesthetic signatures which include estimates of subjective image aesthetics across multiple dimensions.
2. Description of the Related Art
High-quality cameras, either stand-alone or integrated into other devices (e.g., mobile devices), as well as image editing tools, have become increasingly prevalent. These image acquisition and manipulation devices put more power into the hands of average users. However, obtaining aesthetically-appealing images often requires training and experience that average users often lack.
Automated image aesthetics involves making aesthetic judgments of image quality or appeal using computational techniques. Such aesthetic judgments may help average users capture aesthetically-appealing images by, for example, automatically capturing the images when they are aesthetically appealing according to some criteria (e.g., when persons depicted in the image are smiling). However, automated image aesthetics has often been approached as a learning problem on image features obtained from sets of images, where the task is a binary classification between aesthetically pleasing and not aesthetically pleasing. The accuracy of this and other approaches, when compared to subjective aesthetics judgments (i.e., human judgments of aesthetics), has shown room for improvement in many cases.